Solving Target Set Selection with Bounded Thresholds Faster than $2^n$
Ivan Bliznets, Danil Sagunov

TL;DR
This paper investigates the computational complexity of the Target Set Selection problem, providing faster algorithms for specific cases with bounded thresholds and establishing W[1]-hardness when parameterized by the target size.
Contribution
It introduces faster algorithms for Target Set Selection with constant or bounded thresholds and proves W[1]-hardness when parameterized by the target size.
Findings
Faster algorithms for constant thresholds
Faster algorithms for bounded thresholds
W[1]-hardness when parameterized by target size
Abstract
In this paper we consider the Target Set Selection problem. The problem naturally arises in many fields like economy, sociology, medicine. In the Target Set Selection problem one is given a graph with a function and integers . The goal of the problem is to activate at most vertices initially so that at the end of the activation process there is at least activated vertices. The activation process occurs in the following way: (i) once activated, a vertex stays activated forever; (ii) vertex becomes activated if at least of its neighbours are activated. The problem and its different special cases were extensively studied from approximation and parameterized points of view. For example, parameterizations by the following parameters were studied: treewidth, feedback vertex set, diameter,…
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